{"681161":{"#nid":"681161","#data":{"type":"event","title":"PhD Defense by Yiming Jiang","body":[{"value":"\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E\u0026nbsp;Pricing and Causal Inference Under Networks\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EDate:\u003C\/strong\u003E\u0026nbsp;March 31th, 2025\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ETime:\u003C\/strong\u003E\u0026nbsp;10:30AM \u2013 12:00PM\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EMeeting Link:\u003C\/strong\u003E\u0026nbsp;\u003Ca href=\u0022https:\/\/gatech.zoom.us\/j\/97009427244\u0022\u003Ehttps:\/\/gatech.zoom.us\/j\/97009427244\u003C\/a\u003E\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EYiming Jiang\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EOperations Research PhD Student\u003C\/p\u003E\u003Cp\u003EH. Milton Stewart School of Industrial and Systems Engineering\u003C\/p\u003E\u003Cp\u003EGeorgia Institute of Technology\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ECommittee:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EDr. He Wang (Advisor), H. Milton Stewart School of Industrial and Systems Engineering\u003C\/p\u003E\u003Cp\u003EDr. Yao Xie, H. Milton Stewart School of Industrial and Systems Engineering\u003C\/p\u003E\u003Cp\u003EDr.\u0026nbsp;Ashwin Pananjady, H. Milton Stewart School of Industrial and Systems Engineering\u003C\/p\u003E\u003Cp\u003EDr. Jinglong Zhao, Boston\u0026nbsp;University\u003C\/p\u003E\u003Cp\u003EDr. Christina Yu, Cornell University\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003ENetworks often complicate decision-making and system evaluation due to interactions among interconnected nodes. This thesis addresses two significant problems associated with network effects:\u0026nbsp;dynamic pricing in closed ride-sharing networks (Chapter 2) and estimating the total treatment effect (TTE) under network interference (Chapters 3 and 4).\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EChapter 2 proposes an approximate linear programming method to mitigate the curse of dimensionality\u0026nbsp;in dynamic pricing. The developed approach provides a convex, tractable model for generating dynamic policies. Theoretical analysis demonstrates that this model produces tighter revenue upper bounds compared to traditional fluid approximation methods, with numerical experiments confirming its empirical effectiveness.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EChapter 3 introduces a mixed randomization framework combining Bernoulli randomization and cluster-based randomization, extending conventional A\/B testing methods. This mixed approach yields unbiased TTE estimates under specified conditions. Two methods are developed to implement the proposed design under both known and unknown interference settings, and asymptotic variance bounds for the estimators are derived.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EChapter 4 investigates the pseudo-inverse estimator in scenarios where the interference network is imperfectly measured. The chapter quantifies the resulting bias, establishes a\u0026nbsp;tighter variance bound relative to existing literature, proposes an enhanced variance estimator demonstrating superior performance, and introduces a novel methodology for detecting interference. The effectiveness of these approaches is validated through large-scale real-world experiments.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EPricing and Causal Inference Under Networks\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Pricing and Causal Inference Under Networks"}],"uid":"27707","created_gmt":"2025-03-14 15:25:44","changed_gmt":"2025-03-14 15:26:26","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-03-31T10:30:26-04:00","event_time_end":"2025-03-31T12:00:00-04:00","event_time_end_last":"2025-03-31T12:00:00-04:00","gmt_time_start":"2025-03-31 14:30:26","gmt_time_end":"2025-03-31 16:00:00","gmt_time_end_last":"2025-03-31 16:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ZOOM","extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}